Stellar Cluster Detection using GMM with Deep Variational Autoencoder

TL;DR

This paper introduces an unsupervised machine learning method combining Deep Variational Autoencoder and Gaussian Mixture Model to improve detection of stellar clusters in astronomical images, outperforming existing algorithms.

Contribution

It presents a novel unsupervised approach that leverages deep learning and probabilistic modeling for more effective star cluster detection.

Findings

Method outperforms state-of-the-art algorithms

Effective in noisy and distorted images

Works across various types of star clusters

Abstract

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep Variational Autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion.